1. Field of the Invention
The invention relates to the field of automatic computer analysis of images and in particular to a method, and to the corresponding device for automatic segmentation of images through analysis of their texture.
Texture is an important characteristic for the analysis, segmentation and classification of images, in particular when the latter are of low contrast. Perception of textures is closely related to the visual system: this surface property perceived by the observer is considered to be homogeneous and therefore translation invariant for a zone of the same type. Observation of a texture leaves the same visual impression whatever the portion of the texture observed.
The problem to be resolved when analysing the textures in an image is to mark out the modifications of texture on the basis of an observation window moved over image.
2. Discussion of the Background
Numerous methods have been proposed for analysing textures. These methods can be grouped into four distinct families depending on the parameters analysed:
methods proceeding by analysis of spatial characteristics determine the period of repetition of an elementary motif; PA1 methods proceeding by analysis of the frequency characteristics use Fourier transform computations to search for characteristic frequencies; PA1 methods operating by analysis following coevent matrices using a more complicated formalism, performing, around each point and for all the possible directions, statistical analyses on the grey level repetitions; PA1 methods operating by structuralist analysis, implementing artificial intelligence. PA1 A. GAGALOWICZ "Towards a model of textures" PhD Thesis in mathematical sciences, University of Paris IV-1983. PA1 S. RABOISSON "Numerical image processing by characterisation of textures. Application to the segmentation of echograph images" MSc Thesis in information processing - University of Rennes I-1985. PA1 in an initial phase, a file of several examples of each of several textures is created from sets of characteristic values, each set being representative of a sample of a given texture, PA1 then, in a learning phase, these sets of values are used to adjust by successive approximations, weighting coefficients of a network of automata, until the outputs of this network are as close as possible to a configuration characteristic of a given texture, for all the examples of this texture, each of the textures being associated with a characteristic configuration, PA1 then, in a classification phase, the images are processed by computing sets of characteristic values of zones delimited by an observation window moved in the image, each set of characteristic values of a zone, which set is applied to the input of the network of automata, leading to an output configuration of this network, from which configuration are determined one texture from those learned in the learning phase, and an associated confidence value which is a function of a disparity between the output configuration obtained and that associated with this determined texture.
These methods are described in detail in the following documents:
The existing methods therefore necessitate a modelling of the concept of texture by statistical or structuralist models and these methods fail when the textures present in the image to be analysed are not describable by the model and therefore cannot be marked out.